Based on these definitions, the authors of [1]} further classify the tl approaches into three main categories, considering not only the different situations concerning the source and target domains but also the corresponding tasks. In the inductive tl, the source and the target tasks are different (\(\mathcal {T}_S \ne \mathcal {T}_T\) ), no matter whether the source and target domains are the same or not; at least a few labeled data in the target domain are required as the training data to induce the target predictive function. In the case of transductive tl, instead, the source and the target distributions are different due to selection bias or distribution mismatch (\(\mathcal {X}_S \ne \mathcal {X}_T\) ). Finally, in the unsupervised tl, both the domains and the tasks are different (\(\mathcal {X}_S \ne \mathcal {X}_T\)  and \(\mathcal {T}_S \ne \mathcal {T}_T\) ) and, in general, labels are not available neither for the source nor for the target. The goal is to exploit the (unlabeled) information in the source domain to solve unsupervised learning tasks in the target domains. Some tasks belonging to the latter category are clustering, dimensional reduction and density estimation [2]}, [3]}. According to this classification, da methods are transductive tl solutions with the assumption that the tasks are the same, i.e., \(\mathcal {T}_S = \mathcal {T}_T\)  and the differences are only caused by domain divergence, i.e., \(\mathcal {D}_S \ne \mathcal {D}_T\) .
